1. Field of the Invention
This invention relates in general to database management systems performed by computers, and in particular, to the building of predictive models for a Customer Relationship Management (CRM) system that uses a Relational Database Management System (RDBMS).
2. Description of Related Art
In a data warehouse environment, the function of interactive business analysis is supported by a variety of applications and tools, including RDBMS (Relational DataBase Management System) and OLAP (On-Line Analytic Processing) tools. Typically, such business analysis tools use metadata to represent business concepts, and to provide a mapping from the business concepts to data stored in the data warehouse. A business analyst can then use familiar business terms to request an analytic function, and the tool will convert the business terms to the appropriate Table/Column names, and generate and then execute the appropriate SQL to perform the function. Thus, the analyst can request a report on “Sales” for “Eastern Region,” where “Sales” is a “Measure” and “Eastern Region” is a “Segment.” In this example, Segments and Measures are types of metadata, wherein Measures are values or expressions that are useful in reviewing, analyzing or reporting on data elements represented by segments.
The Measures supported by current analytic tools are either simple aggregations (e.g., “Revenues”), or more complex derivations based on formulas (e.g., “Margin”=“Revenues—Costs,” and “Percent Margin”=“Margin/Revenues*100”). In each case, the definitions for the Measures is provided by a human, e.g., by a business analyst during a set-up process that occurs following installation but prior to execution of the tool. Usually, the definitions for the Measures comprise a manual metadata definition process.
A Measure might be predictive, e.g., rather than measure past performance or behavior, it might predict future performance or behavior, typically in the form of a propensity score. For example, it might predict the propensity of a Customer Segment to purchase a product or to terminate service. The formula for a predictive Measure might be provided by a human, based on prior experience or intuition.
A more rigorous approach would be to use a predictive modeling system, the output of which is typically a predictive model which may or may not be in some executable form. Typically, in order to use such a model as a predictive Measure in a business analysis tool, it would be necessary for a human to translate the predictive model formula into an appropriate form (e.g., as SQL statements) that can be processed by the business analysis tool.
Unfortunately, predictive modeling systems are technically complex, and require a high level of statistical and data mining skills to create successful models, including knowledge about the algorithms involved and how they operate. They also typically require expert knowledge of the data involved in the prediction, and programming skills in order to manipulate the data into a form that the predictive modeling system requires.
There is a need then to make data mining algorithms more accessible and more available to business people. There is a strong requirement for marketing campaign planners with modest technical skills (e.g., business analysts) to be able to build predictive models directly in support of their business tasks. Furthermore, once predictive models have been produced, they typically become less effective over time (“model decay”), since the behavior they model becomes outdated as time passes and conditions change. There is thus a further need for business analysts to be able to modify, update, or “tune” existing predictive models.